
The wide application of machine
learning models in the real world
brings an emerging
challenge of understanding the
performance of a machine learning
model under different data
distributions —
ML systems operating autonomous vehicles which are trained based on data collected in the northern hemisphere might fail when deployed in desert-like environments or under different weather conditions [1]}, [2]},
while recognition systems fail when deployed in new environments [3]}.
Similar concerns also apply to many mission-critical applications
such as medicine and cyber-security [4]}, [5]}, [6]}.
In all these applications,
it is imperative to have a sound understanding of the model's robustness and possible failure cases in the presence of a shift in data distribution and to have corresponding guarantees on the model's performance.
